Since scipy was mentioned quite often, there is GitHub - AtsushiSakai/SciPy.jl: Julia interface for SciPy which technically makes Julia as good as Python when using scipy 
As for courses, creating a curated list of packages useful for a course in a Project.toml and Manifest.toml files is not difficult. If you are teaching the course and you choose Julia as the programming language to recommend, the burden of finding which tools to use in Julia lies mostly on you as the instructor and not on the students. So if the students are spending too much time finding the right package, I think a cheat sheet of useful packages might be useful to add to the course material. Perhaps some of the course preparation time can also go into improving the documentation of the packages to be used in the course or adding more examples and tutorials.
As for finding good packages, I think test and documentation coverage are 2 things to look for. Maybe we need a tool for quantifying how much of the exported API is covered in the docs.